Improved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network

Journal article

Yan, C., Li, M., Liu, W. and Qi, M. 2020. Improved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network. Theoretical Computer Science. 817, pp. 12-23. https://doi.org/10.1016/j.tcs.2019.06.025
AuthorsYan, C., Li, M., Liu, W. and Qi, M.
Abstract

With the development of the insurance industry, insurance fraud is increasing rapidly. The existence of insurance fraud considerably hinders the development of the insurance industry. Fraud identification has become the most important part of insurance fraud research. In this paper, an improved adaptive genetic algorithm (NAGA) combined with a BP neural network (BP neural network) is proposed to optimize the initial weight of BP neural networks to overcome their shortcomings, such as ease of falling into local minima, slow convergence rates and sample dependence. Finally, the historical automobile insurance claim data of an insurance company are taken as a sample. The NAGA-BP neural network model was used for simulation and prediction. The empirical results show that the improved genetic algorithm is more advanced than the traditional genetic algorithm in terms of convergence speed and prediction accuracy.

KeywordsGenetic algorithm; Neural network; Insurance fraud
Year2020
JournalTheoretical Computer Science
Journal citation817, pp. 12-23
PublisherElsevier
ISSN0304-3975
Digital Object Identifier (DOI)https://doi.org/10.1016/j.tcs.2019.06.025
Official URLhttps://www.sciencedirect.com/science/article/abs/pii/S0304397519304177
Publication dates
Online03 Jul 2019
Print12 May 2020
Publication process dates
Accepted06 Jun 2019
Deposited20 May 2021
Accepted author manuscript
License
Output statusPublished
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